Presently, there is a vast amount of media content, such as audios, videos, or graphics, available from a variety of sources. From digital graphics and music to films or movies to broadcast television programs to cable or satellite television programs to home movies or user-created video clips, there are many repositories and databases from which people may choose and obtain media content in various formats, and the amount of media content available continues to grow at a very high rate. Broadcast, cable, or satellite companies often provide hundreds of different channels for viewers to choose from. Movie rental companies such as Netflix and Blockbuster have tens, even hundreds, of thousands of titles on DVDs (digital video disc) or video cassettes. More recently, the Internet has also lent its unique capability and become a great repository and distribution channel for video media world-wide. Online stores such as Amazon.com have a great number of CDs, DVDs, and downloadable media files for sale. Websites such as YouTube and AOL Video have immense audio and video collections, often millions of audio/video clips, contributed by users from all over the world.
With such a great amount of available media content, often there is the need to rank a selected set of media content. For example, suppose a person is looking for video clips relating to the subject matter of figure skating at YouTube's website. The person searches for the video clips using the keywords “figure skating,” and may currently be presented with nearly sixty thousand choices. Obviously, it is impractical and nearly impossible to present all sixty thousand video clips to the person simultaneously. Instead, the video clips are presented in a sequential order, perhaps a few at a time. YouTube may choose to display twenty video clips on each web page and enable the person to examine and/or view as many video clips as he or she chooses by going through multiple web pages. In this case, the nearly sixty thousand video clips need to be ranked first so that they may be presented to the person in sequence. For example, YouTube may rank the video clips according to their relevance to the subject matter of figure skating, e.g. more relevant video clips ranked higher, or according to their posting dates, e.g., newer video clips ranked higher. Other ranking methods include ranking according to popularity, by alphabetical order, etc.
In another similar example, suppose a person wishes to purchase romantic music in MP3 format from Amazon. The person searches for the downloadable music files using the keywords “romance” at Amazon's website, and may currently be presented with nearly nine thousand songs. Again, the nearly nine thousand songs need to be ranked before being presented to the person in a sequential order, and the ranking may be performed according to relevance, best selling, price, average customer review, release date, etc.
In the above examples, although the rankings are performed for specific persons, i.e., the person searching for the video clips or the music files, the method or criteria used to rank the search results often do not take into consideration the person's own preferences or tastes. In other words, the ranking is not personally tailored for the individual users or customers. Consequently, the resulting orders may not be best suitable for the specific individuals for whom the rankings are performed.